The accuracy of varietal selection using factor analytic models for multi-environment plant breeding trials

Modeling of cultivar x trial effects for multienvironment trials (METs) within a mixed model framework is now common practice in many plant breeding programs. The factor analytic (FA) model is a parsimonious form used to approximate the fully unstructured form of the genetic variance-covariance matrix in the model for MET data. In this study, we demonstrate that the FA model is generally the model of best fit across a range of data sets taken from early generation trials in a breeding program. In addition, we demonstrate the superiority of the FA model in achieving the most common aim of METs, namely the selection of superior genotypes. Selection is achieved using best linear unbiased predictions (BLUPs) of cultivar effects at each environment, considered either individually or as a weighted average across environments. In practice, empirical BLUPs (E-BLUPs) of cultivar effects must be used instead of BLUPs since variance parameters in the model must be estimated rather than assumed known. While the optimal properties of minimum mean squared error of prediction (MSEP) and maximum correlation between true and predicted effects possessed by BLUPs do not hold for E-BLUPs, a simulation study shows that E-BLUPs perform well in terms of MSEP.

Estimation in a multiplicative mixed model involving a genetic relationship matrix.

Genetic models partitioning additive and non-additive genetic effects for populations tested in replicated multi-environment trials (METs) in a plant breeding program have recently been presented in the literature. For these data, the variance model involves the direct product of a large numerator relationship matrix A, and a complex structure for the genotype by environment interaction effects, generally of a factor analytic (FA) form. With MET data, we expect a high correlation in genotype rankings between environments, leading to non-positive definite covariance matrices. Estimation methods for reduced rank models have been derived for the FA formulation with independent genotypes, and we employ these estimation methods for the more complex case involving the numerator relationship matrix. We examine the performance of differing genetic models for MET data with an embedded pedigree structure, and consider the magnitude of the non-additive variance. The capacity of existing software packages to fit these complex models is largely due to the use of the sparse matrix methodology and the average information algorithm. Here, we present an extension to the standard formulation necessary for estimation with a factor analytic structure across multiple environments.

Development of profitable milk production systems for northern Australia: a field assessment of the productivity of five potential farming systems using farmlets.

Farmlets, each of 20 cows, were established to field test five milk production systems and provide a learning platform for farmers and researchers in a subtropical environment. The systems were developed through desktop modelling and industry consultation in response to the need for substantial increases in farm milk production following deregulation of the industry. Four of the systems were based on grazing and the continued use of existing farmland resource bases, whereas the fifth comprised a feedlot and associated forage base developed as a greenfield site. The field evaluation was conducted over 4 years under more adverse environmental conditions than anticipated with below average rainfall and restrictions on irrigation. For the grazed systems, mean annual milk yield per cow ranged from 6330 kg/year (1.9 cows/ha) for a herd based on rain-grown tropical pastures to 7617 kg/year (3.0 cows/ha) where animals were based on temperate and tropical irrigated forages. For the feedlot herd, production of 9460 kg/cow.year (4.3 cows/ha of forage base) was achieved. For all herds, the level of production achieved required annual inputs of concentrates of similar to 3 t DM/animal and purchased conserved fodder from 0.3 to 1.5 t DM/animal. This level of supplementary feeding made a major contribution to total farm nutrient inputs, contributing 50% or more of the nitrogen, phosphorus and potassium entering the farming system, and presents challenges to the management of manure and urine that results from the higher stocking rates enabled. Mean annual milk production for the five systems ranged from 88 to 105% of that predicted by the desktop modelling. This level of agreement for the grazed systems was achieved with minimal overall change in predicted feed inputs; however, the feedlot system required a substantial increase in inputs over those predicted. Reproductive performance for all systems was poorer than anticipated, particularly over the summer mating period. We conclude that the desktop model, developed as a rapid response to assist farmers modify their current farming systems, provided a reasonable prediction of inputs required and milk production. Further model development would need to consider more closely climate variability, the limitations summer temperatures place on reproductive success and the feed requirements of feedlot herds.

Net radiation dynamics: performance of 20 daily net radiation models as related to model structure and intricacy in two climates.

We compared daily net radiation (Rn) estimates from 19 methods with the ASCE-EWRI Rn estimates in two climates: Clay Center, Nebraska (sub-humid) and Davis, California (semi-arid) for the calendar year. The performances of all 20 methods, including the ASCE-EWRI Rn method, were then evaluated against Rn data measured over a non-stressed maize canopy during two growing seasons in 2005 and 2006 at Clay Center. Methods differ in terms of inputs, structure, and equation intricacy. Most methods differ in estimating the cloudiness factor, emissivity (e), and calculating net longwave radiation (Rnl). All methods use albedo (a) of 0.23 for a reference grass/alfalfa surface. When comparing the performance of all 20 Rn methods with measured Rn, we hypothesized that the a values for grass/alfalfa and non-stressed maize canopy were similar enough to only cause minor differences in Rn and grass- and alfalfa-reference evapotranspiration (ETo and ETr) estimates. The measured seasonal average a for the maize canopy was 0.19 in both years. Using a = 0.19 instead of a = 0.23 resulted in 6% overestimation of Rn. Using a = 0.19 instead of a = 0.23 for ETo and ETr estimations, the 6% difference in Rn translated to only 4% and 3% differences in ETo and ETr, respectively, supporting the validity of our hypothesis. Most methods had good correlations with the ASCE-EWRI Rn (r2 > 0.95). The root mean square difference (RMSD) was less than 2 MJ m-2 d-1 between 12 methods and the ASCE-EWRI Rn at Clay Center and between 14 methods and the ASCE-EWRI Rn at Davis. The performance of some methods showed variations between the two climates. In general, r2 values were higher for the semi-arid climate than for the sub-humid climate. Methods that use dynamic e as a function of mean air temperature performed better in both climates than those that calculate e using actual vapor pressure. The ASCE-EWRI-estimated Rn values had one of the best agreements with the measured Rn (r2 = 0.93, RMSD = 1.44 MJ m-2 d-1), and estimates were within 7% of the measured Rn. The Rn estimates from six methods, including the ASCE-EWRI, were not significantly different from measured Rn. Most methods underestimated measured Rn by 6% to 23%. Some of the differences between measured and estimated Rn were attributed to the poor estimation of Rnl. We conducted sensitivity analyses to evaluate the effect of Rnl on Rn, ETo, and ETr. The Rnl effect on Rn was linear and strong, but its effect on ETo and ETr was subsidiary. Results suggest that the Rn data measured over green vegetation (e.g., irrigated maize canopy) can be an alternative Rn data source for ET estimations when measured Rn data over the reference surface are not available. In the absence of measured Rn, another alternative would be using one of the Rn models that we analyzed when all the input variables are not available to solve the ASCE-EWRI Rn equation. Our results can be used to provide practical information on which method to select based on data availability for reliable estimates of daily Rn in climates similar to Clay Center and Davis.

Improving management of summer weeds in dryland cropping systems with Cotton

In 2001 a scoping study (phase I) was commissioned to determine and prioritise the weed issues of cropping systems with dryland cotton. The main findings were that the weed flora was diverse, cropping systems complex, and weeds had a major financial and economical impact. Phase II 'Best weed management strategies for dryland cropping systems with cotton' focused on improved management of the key weeds, bladder ketmia, sowthistle, fleabane, barnyard grass and liverseed grass.In Phase III 'Improving management of summer weeds in dryland cropping systems with cotton', more information on the seed-bank dynamics of key weeds was gained in six pot and field studies. The studies found that these characteristics differed between species, and even climate in the case of bladder ketmia. Species such as sowthistle, fleabane and barnyard grass emerged predominately from the surface soil. Sweet summer grass was also in this category but also had a significant proportion emerging from 5 cm depth. Bladder ketmia in central Queensland emerged mainly from the top 2 cm, whereas in southern Queensland it emerged mainly from 5 cm. Liverseed grass had its highest emergence from 5 cm below the surface. In all cases the persistence of seed increased with increasing soil depth. Fleabane was also found to be sensitive to soil type with no seedlings emerging in the self-mulching black vertisol soil. A strategic tillage trial showed that burial of fleabane seed, using a disc or chisel plough, to a depth of greater than 2 cm can significantly reduce subsequent fleabane emergence. In contrast, tillage increased barnyard grass emergence and tended to decrease persistence. This research showed that weed management plans can not be blanketed across all weed species, rather they need to be targeted for each main weed species.This project has also resulted in an increased knowledge of how to manage fleabane from the eight experiments; one in wheat, two in sorghum, one in cotton and three in fallow on double knock. For summer crops, the best option is to apply a highly effective fallow treatment prior to sowing the crops. For winter crops, the strategy is the integration of competitive crops, residual herbicide followed by a knockdown to control survivors. This project explored further the usefulness of the double knock tactic for weed control and preventing seed set. Two field and one pot experiments have shown that this tactic was highly effective for fleabane control. Paraquat products provided good control when followed by glyphosate. When 2, 4-D was added in a tank mix with glyphosate and followed by paraquat products, 99-100% control was achieved in all cases. The ideal follow-up times for paraquat products after glyphosate were 5-7 days. The preferred follow-up times for 2, 4-D after glyphosate were on the same day and one day later. The pot trial, which compared a population from a cropping field with previous glyphosate exposure and a population from a non-cropping area with no previous glyphosate herbicide exposure, showed that the pervious herbicide exposure affected the response of fleabane to herbicidal control measures. The web-based brochure on managing fleabane has been updated.Knowledge on management of summer grasses and safe use of residual herbicides was derived from eight field and pot experiments. Residual grass and broadleaf weed control was excellent with atrazine pre-plant and at-planting treatments, provided rain was received within a short interval after application. Highly effective fallow treatments (cultivation and double knock), not only gave excellent grass control in the fallow, also gave very good control in the following cotton. In the five re-cropping experiments, there were no adverse impacts on cotton from atrazine, metolachlor, metsulfuron and chlorsulfuron residues following use in previous sorghum, wheat and fallows. However, imazapic residues did reduce cotton growth.The development of strategies to reduce the heavy reliance on glyphosate in our cropping systems, and therefore minimise the risk of glyphosate resistance development, was a key factor in the research undertaken. This work included identifying suitable tactics for summer grass control, such as double knock with glyphosate followed by paraquat and tillage. Research on fleabane also concentrated on minimising emergence through tillage, and applying the double knock tactic. Our studies have shown that these strategies can be used to prevent seed set with the goal of driving down the seed bank. Utilisation of the strategies will also reduce the reliance on glyphosate, and therefore reduce the risk of glyphosate resistance developing in our cropping systems.Information from this research, including ecological and management data were collected from an additional eight paddock monitoring sites, was also incorporated into the Weeds CRC seed bank model "Weed Seed Wizard", which will be able to predict the impact of different management options on weed populations in cotton and grain farming systems. Extensive communication activities were undertaken throughout this project to ensure adoption of the new strategies for improved weed management and reduced risk for glyphosate resistance.

Characterization of north-eastern Australian environments using APSIM for increasing rainfed maize production

Recurring water stresses are a major risk factor for rainfed maize cropping across the highly diverse agro-ecological environments of Queensland (Qld) and northern New South Wales (NNSW). Enhanced understanding of such agro-ecological diversity is necessary to more consistently sample target production environments for testing and targeting release of improved germplasm, and to improve the efficiency of the maize pre-breeding and breeding programs of Qld and New South Wales. Here, we used the Agricultural Production Systems Simulator (APSIM) – a well validated maize crop model to characterize the key distinctive water stress patterns and risk to production across the main maize growing regions of Qld and NNSW located between 15.8° and 31.5°S, and 144.5° and 151.8°E. APSIM was configured to simulate daily water supply demand ratios (SDRs) around anthesis as an indicator of the degree of water stress, and the final grain yield. Simulations were performed using daily climatic records during the period between 1890 and 2010 for 32 sites-soils in the target production regions. The runs were made assuming adequate nitrogen supply for mid-season maize hybrid Pioneer 3153. Hierarchical complete linkage analyses of the simulated yield resulted in five major clusters showing distinct probability distribution of the expected yields and geographic patterns. The drought stress patterns and their frequencies using SDRs were quantified using multivariate statistical methods. The identified stress patterns included no stress, mid-season (flowering) stress, and three terminal stresses differing in terms of severity. The combined frequency of flowering and terminal stresses was highest (82.9%), mainly in sites-soils combinations in the west of Qld and NNSW. Yield variability across the different sites-soils was significantly related to the variability in frequencies of water stresses. Frequencies of water stresses within each yield cluster tended to be similar, but different across clusters. Sites-soils falling within each yield cluster therefore could be treated as distinct maize production environments for testing and targeting newly developed maize cultivars and hybrids for adaptation to water stress patterns most common to those environments.

Inheritance of resistance to root-lesion nematodes (Pratylenchus thornei and P. neglectus) in five doubled-haploid populations of wheat

Nematode species Pratylenchus thornei and P. neglectus are the two most important root-lesion nematodes affecting wheat (Triticum aestivum L.) and other grain crops in Australia. For practical plant breeding, it will be valuable to know the mode of inheritance of resistance and whether the same set of genes confer resistance to both species. We evaluated reactions to P. thornei and P. neglectus of glasshouse-inoculated plants of five doubled-haploid populations derived from five resistant synthetic hexpaloid wheat lines, each crossed to the susceptible Australian wheat cultivar Janz. For each cross we determined genetic variance, heritability and minimum number of effective resistance genes for each nematode species. Distributions of nematode numbers for both species were continuous for all doubled-haploid populations. Heritabilities were high and the resistances were controlled by 4-7 genes. There was no genetic correlation between resistance to P. thornei and to P. neglectus in four of the populations and a significant but low correlation in one. Therefore, resistances to P. thornei and to P. neglectus are probably inherited quantitatively and independently in four of these synthetic hexaploid wheat populations, with the possibility of at least one genetic factor contributing to resistance to both species in one of the populations. Parents with the greatest level of resistance will be the best to use as donor parents to adapted cultivars, and selection of resistance to both species in early generations will be optimal to carry resistance through successive cycles of inbreeding to produce resistant cultivars for release.

Dosage consistency is the key factor in avoiding evolution of resistance to phosphine and population increase in stored-grain pests

BACKGROUND Control of pests in stored grain and the evolution of resistance to pesticides are serious problems worldwide. A stochastic individual-based two-locus model was used to investigate the impact of two important issues, the consistency of pesticide dosage through the storage facility and the immigration rate of the adult pest, on overall population control and avoidance of evolution of resistance to the fumigant phosphine in an important pest of stored grain, the lesser grain borer. RESULTS A very consistent dosage maintained good control for all immigration rates, while an inconsistent dosage failed to maintain control in all cases. At intermediate dosage consistency, immigration rate became a critical factor in whether control was maintained or resistance emerged. CONCLUSION Achieving a consistent fumigant dosage is a key factor in avoiding evolution of resistance to phosphine and maintaining control of populations of stored-grain pests; when the dosage achieved is very inconsistent, there is likely to be a problem regardless of immigration rate. © 2012 Society of Chemical Industry

Prediction of breeding values for average fruit weight in mango using a multivariate individual mixed model

Mango is an important horticultural fruit crop and breeding is a key strategy to improve ongoing sustainability. Knowledge of breeding values of potential parents is important for maximising progress from breeding. This study successfully employed a mixed linear model methods incorporating a pedigree to predict breeding values for average fruit weight from highly unbalanced data for genotypes planted over three field trials and assessed over several harvest seasons. Average fruit weight was found to be under strong additive genetic control. There was high correlation between hybrids propagated as seedlings and hybrids propagated as scions grafted onto rootstocks. Estimates of additive genetic correlation among trials ranged from 0.69 to 0.88 with correlations among harvest seasons within trials greater than 0.96. These results suggest that progress from selection for broad adaptation can be achieved, particularly as no repeatable environmental factor that could be used to predict G x E could be identified. Predicted breeding values for 35 known cultivars are presented for use in ongoing breeding programs.

Targeting resource investments to achieve sediment reduction and improved Great Barrier Reef health

Concerns about excessive sediment loads entering the Great Barrier Reef (GBR) lagoon in Australia have led to a focus on improving ground cover in grazing lands. Ground cover has been identified as an important factor in reducing sediment loads, but improving ground cover has been difficult for reef stakeholders in major catchments of the GBR. To provide better information an optimising linear programming model based on paddock scale information in conjunction with land type mapping was developed for the Fitzroy, the largest of the GBR catchments. This identifies at a catchment scale which land types allow the most sediment reduction to be achieved at least cost. The results suggest that from the five land types modelled, the lower productivity land types present the cheapest option for sediment reductions. The study allows more informed decision making for natural resource management organisations to target investments. The analysis highlights the importance of efficient allocation of natural resource management funds in achieving sediment reductions through targeted land type investments. © 2012.

Evaluation and histological examination of a Campylobacter fetus subsp. venerealis small animal infection model

Bovine genital campylobacteriosis (BGC), caused by Campylobacter fetus subsp. venerealis, is associated with production losses in cattle worldwide. This study aimed to develop a reliable BGC guinea pig model to facilitate future studies of pathogenicity, abortion mechanisms and vaccine efficacy. Seven groups of five pregnant guinea pigs (1 control per group) were inoculated with one of three strains via intra-peritoneal (IP) or intra-vaginal routes. Samples were examined using culture, PCR and histology. Abortions ranged from 0 to 100 and re-isolation of causative bacteria from sampled sites varied with strain, dose of bacteria and time to abortion. Histology indicated metritis and placentitis, suggesting that the bacteria induce inflammation, placental detachment and subsequent abortion. Variation of virulence between strains was observed and determined by culture and abortion rates. IP administration of C. fetus subsp. venerealis to pregnant guinea pigs is a promising small animal model for the investigation of BGC abortion.

An improved simulation model to predict pre-harvest aflatoxin risk in maize

Aflatoxin is a potent carcinogen produced by Aspergillus flavus, which frequently contaminates maize (Zea mays L.) in the field between 40° north and 40° south latitudes. A mechanistic model to predict risk of pre-harvest contamination could assist in management of this very harmful mycotoxin. In this study we describe an aflatoxin risk prediction model which is integrated with the Agricultural Production Systems Simulator (APSIM) modelling framework. The model computes a temperature function for A. flavus growth and aflatoxin production using a set of three cardinal temperatures determined in the laboratory using culture medium and intact grains. These cardinal temperatures were 11.5 °C as base, 32.5 °C as optimum and 42.5 °C as maximum. The model used a low (≤0.2) crop water supply to demand ratio—an index of drought during the grain filling stage to simulate maize crop's susceptibility to A. flavus growth and aflatoxin production. When this low threshold of the index was reached the model converted the temperature function into an aflatoxin risk index (ARI) to represent the risk of aflatoxin contamination. The model was applied to simulate ARI for two commercial maize hybrids, H513 and H614D, grown in five multi-location field trials in Kenya using site specific agronomy, weather and soil parameters. The observed mean aflatoxin contamination in these trials varied from <1 to 7143 ppb. ARI simulated by the model explained 99% of the variation (p ≤ 0.001) in a linear relationship with the mean observed aflatoxin contamination. The strong relationship between ARI and aflatoxin contamination suggests that the model could be applied to map risk prone areas and to monitor in-season risk for genotypes and soils parameterized for APSIM.

An improved simulation model to predict pre-harvest aflatoxin risk in maize

Aflatoxin is a potent carcinogen produced by Aspergillus flavus, which frequently contaminates maize (Zea mays L.) in the field between 40° north and 40° south latitudes. A mechanistic model to predict risk of pre-harvest contamination could assist in management of this very harmful mycotoxin. In this study we describe an aflatoxin risk prediction model which is integrated with the Agricultural Production Systems Simulator (APSIM) modelling framework. The model computes a temperature function for A. flavus growth and aflatoxin production using a set of three cardinal temperatures determined in the laboratory using culture medium and intact grains. These cardinal temperatures were 11.5 °C as base, 32.5 °C as optimum and 42.5 °C as maximum. The model used a low (≤0.2) crop water supply to demand ratio—an index of drought during the grain filling stage to simulate maize crop's susceptibility to A. flavus growth and aflatoxin production. When this low threshold of the index was reached the model converted the temperature function into an aflatoxin risk index (ARI) to represent the risk of aflatoxin contamination. The model was applied to simulate ARI for two commercial maize hybrids, H513 and H614D, grown in five multi-location field trials in Kenya using site specific agronomy, weather and soil parameters. The observed mean aflatoxin contamination in these trials varied from <1 to 7143 ppb. ARI simulated by the model explained 99% of the variation (p ≤ 0.001) in a linear relationship with the mean observed aflatoxin contamination. The strong relationship between ARI and aflatoxin contamination suggests that the model could be applied to map risk prone areas and to monitor in-season risk for genotypes and soils parameterized for APSIM.

Otolith morphology of four mackerel species (Scomberomorus spp.) in Australia: Species differentiation and prediction for fisheries monitoring and assessment

Four species of large mackerels (Scomberomorus spp.) co-occur in the waters off northern Australia and are important to fisheries in the region. State fisheries agencies monitor these species for fisheries assessment; however, data inaccuracies may exist due to difficulties with identification of these closely related species, particularly when specimens are incomplete from fish processing. This study examined the efficacy of using otolith morphometrics to differentiate and predict among the four mackerel species off northeastern Australia. Seven otolith measurements and five shape indices were recorded from 555 mackerel specimens. Multivariate modelling including linear discriminant analysis (LDA) and support vector machines, successfully differentiated among the four species based on otolith morphometrics. Cross validation determined a predictive accuracy of at least 96% for both models. An optimum predictive model for the four mackerel species was an LDA model that included fork length, feret length, feret width, perimeter, area, roundness, form factor and rectangularity as explanatory variables. This analysis may improve the accuracy of fisheries monitoring, the estimates based on this monitoring (i.e. mortality rate) and the overall management of mackerel species in Australia.